On-line self-adaptive framework for tailoring a neural-agent learning model addressing dynamic real-time scheduling problems

2017 ◽  
Vol 45 ◽  
pp. 97-108 ◽  
Author(s):  
Zeineb Hammami ◽  
Wiem Mouelhi ◽  
Lamjed Ben Said
Author(s):  
Jian (Denny) Lin ◽  
Albert M. K. Cheng ◽  
Doug Steel ◽  
Michael Yu-Chi Wu ◽  
Nanfei Sun

Enabling computer tasks with different levels of criticality running on a common hardware platform has been an increasingly important trend in the design of real-time and embedded systems. On such systems, a real-time task may exhibit different WCETs (Worst Case Execution Times) in different criticality modes. It is well-known that traditional real-time scheduling methods are not applicable to ensure the timely requirement of the mixed-criticality tasks. In this paper, the authors study a problem of scheduling real-time, mixed-criticality tasks with fault tolerance. An optimal, off-line algorithm is designed to guarantee the most tasks completing successfully when the system runs into the high-criticality mode. A formal proof of the optimality is given. Also, a novel on-line slack-reclaiming algorithm is proposed to recover from computing faults before the tasks' deadline during the run-time. Simulations show that an improvement of about 30% in performance is obtained by using the slack-reclaiming method.


2012 ◽  
Vol 23 (4) ◽  
pp. 996-1009
Author(s):  
Dong-Song ZHANG ◽  
Tong WU ◽  
Fang-Yuan CHEN ◽  
Shi-Yao JIN

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